{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img align=\"center\" src=\"./images/occiput_logo3.png\" width=\"400\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load dynamic sinogram from listmode PET data\n",
    "This if useful e.g. for kinetic imaging and motion correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import occiput\n",
    "from occiput.Reconstruction.PET.PET_scanners import Biograph_mMR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pet = occiput.Reconstruction.PET.PET_Dynamic_Scan()\n",
    "pet.set_scanner(Biograph_mMR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bin the listmode data into a series of sinograms\n",
    "#### All it takes is to define an array with the trigger events in [ms]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time_range = [0,1000,2000,3000]\n",
    "filename = '../data/PET_data/scans/2014_12_03_cylinder/listmode/PET_ACQ_342_20141212155111-0.l.hdr'\n",
    "pet.load_listmode_file(filename, time_range_ms=time_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uncompression done\n",
      "Uncompression done\n",
      "Uncompression done\n",
      "Uncompression done\n",
      "Uncompression done\n",
      "Uncompression done\n",
      "Uncompression done\n",
      "Uncompression done\n"
     ]
    }
   ],
   "source": [
    "prompts0 = pet.frame0.prompts.to_nd_array()\n",
    "randoms0 = pet.frame0.randoms.to_nd_array()\n",
    "\n",
    "prompts1 = pet.frame1.prompts.to_nd_array()\n",
    "randoms1 = pet.frame1.randoms.to_nd_array()\n",
    "\n",
    "prompts2 = pet.frame2.prompts.to_nd_array()\n",
    "randoms2 = pet.frame2.randoms.to_nd_array()\n",
    "\n",
    "prompts_global = pet.static.prompts.to_nd_array()\n",
    "randoms_global = pet.static.randoms.to_nd_array()\n",
    "\n",
    "print prompts0.sum()\n",
    "print randoms0.sum()\n",
    "\n",
    "print prompts1.sum()\n",
    "print randoms1.sum()\n",
    "\n",
    "print prompts2.sum()\n",
    "print randoms2.sum()\n",
    "\n",
    "print prompts_global.sum()\n",
    "print randoms_global.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Under the hood, the sinograms (prompts and randoms) are stored in C structures that reside in the global scope of the \n",
    "library listmode_c.so. The Python wrapper exposes methods to obtain additional information about these C structures, \n",
    "such as compression ratio. This information can be useful for debugging. Let's print the information for the first sinogram (index 0): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'angles_axial': 0.0, 'time_start': 0L, 'N_u': 344L, 'N_v': 127L, 'N_counts': 244289L, 'compression_ratio': 0.0020062404219061136, 'N_locations': 242962L, 'time_bin': 0L, 'listmode_loss': 1.0054618120193481, 'angles_azimuthal': 0.0, 'N_azimuthal': 11L, 'size_u': 717.6734619140625, 'size_v': 257.96875, 'N_axial': 252L, 'time_end': 1000L}\n",
      "{'angles_axial': 0.0, 'time_start': 0L, 'N_u': 344L, 'N_v': 127L, 'N_counts': 48713L, 'compression_ratio': 0.00040210355655290186, 'N_locations': 48696L, 'time_bin': 0L, 'listmode_loss': 1.0003491640090942, 'angles_azimuthal': 0.0, 'N_azimuthal': 11L, 'size_u': 717.6734619140625, 'size_v': 257.96875, 'N_axial': 252L, 'time_end': 1000L}\n"
     ]
    }
   ],
   "source": [
    "print pet.scanner.listmode.get_measurement_prompt_info(0)\n",
    "print pet.scanner.listmode.get_measurement_delay_info(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uncompression done\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x71f1490>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tb4mZXQwcBVy1j9UjgBZ3356xvAYYmYt4pPPGnBSO1z7kMP1QSeHb8LJxx5yyonsMc+NS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x637eb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(pet.frame0.prompts.to_nd_array()[0,5,:,:].T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### Note that the pet object can be indexed and iterated. We can use pet[0] instead of  pet.frame0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uncompression done\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x71f6350>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HPEW6QglByrhTAw6Zo1sKyQNzDjsvYPQJbZ9vjXXQVJfHmLpgxJEBh1+UpKxy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x636dcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(pet[0].prompts.to_nd_array()[0,5,:,:].T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
